Constrained structural design optimization via a parallel augmented Lagrangian particle swarm optimization approach

2011 ◽  
Vol 89 (13-14) ◽  
pp. 1352-1366 ◽  
Author(s):  
P.W. Jansen ◽  
R.E. Perez
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Structural Design ◽  
Augmented Lagrangian ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Lagrangian Particle ◽  
Swarm Optimization ◽  
Structural Design Optimization

A new hybrid particle swarm optimization approach for structural design optimization in the automotive industry

2012 ◽  
Vol 226 (10) ◽  
pp. 1340-1351 ◽  
Author(s):  
Ali R Yildiz
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Automotive Industry ◽  
Structural Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization ◽  
Structural Design Optimization

This paper presents an innovative optimization approach to solve structural design optimization problems in the automotive industry. The new approach is based on Taguchi’s robust design approach and particle swarm optimization algorithm. The proposed approach is applied to the structural design optimization of a vehicle part to illustrate how the present approach can be applied for solving design optimization problems. The results show the ability of the proposed approach to find better optimal solutions for structural design optimization problems.

Post-nonlinear blind source separation with kurtosis constraints using augmented Lagrangian particle swarm optimization and its application to mechanical systems

2019 ◽  
Vol 25 (16) ◽  
pp. 2246-2260 ◽  
Author(s):  
Jiantao Lu ◽  
Wei Cheng ◽  
Yapeng Chu ◽  
Yanyang Zi
Keyword(s):  
Particle Swarm Optimization ◽  
Case Studies ◽  
Blind Source Separation ◽  
Augmented Lagrangian ◽  
Source Separation ◽  
Particle Swarm ◽  
Lagrangian Multiplier ◽  
Lagrangian Particle ◽  
Swarm Optimization ◽  
Source Signals

To accurately estimate source signals from their post-nonlinear mixtures, a post-nonlinear blind source separation (PNLBSS) method with kurtosis constraints is proposed based on augmented Lagrangian particle swarm optimization (PSO). First, an improved contrast function is presented by combining mutual information of the separated signals and kurtosis ranges of source signals. Second, an augmented Lagrangian multiplier method is used to convert PNLBSS into an unconstrained pseudo-objective optimization problem. Then, improved PSO is applied to update the parameters in complex nonlinear spaces. Finally, numerical case studies and experimental case studies are provided to evaluate the performance of the proposed method. By adding the kurtosis ranges constraints, the estimation accuracy of source signals could be improved, which would benefit vibration and acoustic monitoring and control.

DESIGN OF PWMSC CONTROLLER USING AUGMENTED LAGRANGIAN PARTICLE SWARM OPTIMIZATION ALGORITHM

2014 ◽  
Vol 23 (08) ◽  
pp. 1450110 ◽  
Author(s):  
AMIN SAFARI ◽  
HEIDAR ALI SHAYANFAR ◽  
HOSSEIN SHAYEGHI ◽  
AMIR AMELI
Keyword(s):  
Particle Swarm Optimization ◽  
Augmented Lagrangian ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Performance Criteria ◽  
Lagrangian Particle ◽  
Nonlinear Simulation ◽  
Swarm Optimization ◽  
Pulse Width Modulated ◽  
The Time Domain

This paper presents a systematic process for designing a damping controller for pulse width modulated series compensator (PWMSC) to improve the angular stability of a multi-machine power system. The proposed controller is robust and tuned by satisfying the multiple H∞ performance criteria to stabilize the system at multiple operating conditions. The design problem has been converted into a constrained nonlinear optimization problem with the time domain-based objective function which is solved by an augmented Lagrangian particle swarm optimization (ALPSO) algorithm. The proposed control scheme has been implemented on two nonlinear test systems. The nonlinear simulation results clearly verify that the designed controller with proposed model improves the dynamic stability of the case studies, particularly when the operating loadings changes.

scholarly journals Método de Otimização por Enxame de Partículas Aplicado ao Despacho Econômico de Geradores

2020 ◽  
Author(s):  
Gabriel Q. Fechine ◽  
Washington L. A. Neves ◽  
Benemar A. Souza
Keyword(s):  
Particle Swarm Optimization ◽  
Augmented Lagrangian ◽  
Particle Swarm ◽  
Lagrangian Particle ◽  
Swarm Optimization

O despacho econômico de geradores tem como objetivo a minimização do custo de produção de energia, o que se dá por meio da melhor distribuição de carga entre as unidades geradoras disponíveis e da minimização das perdas na transmissão mediante algumas restrições. Vários métodos de otimização podem ser utilizados para a resolução de problemas de despacho econômico, sejam estes métodos clássicos ou heurísticos. O método de otimização utilizado neste artigo é o ALPSO (Augmented Lagrangian Particle Swarm Optimization), uma versão ampliada do método PSO (Particle Swarm Optimization) clássico, com a inclusão de restrições por meio de um método baseado nos multiplicadores de Lagrange. Este método será aplicado para a obtenção do despacho econômico em um sistema elétrico de potência e os resultados serão comparados com a solução obtida pelo método clássico dos gradientes. Além disso, serão realizadas análises com diferentes topologias do PSO, comparando o desempenho das mesmas. O método ALPSO obteve resultados similares com implementações mais fáceis, quando comparado ao método dos gradientes.

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